In an environment in which business tasks are to be outsourced or crowdsourced, finding a crowdsourcing platform is often a tedious and cumbersome manual job. In general, systems are designed that enable businesses to do this by using benchmarked data to select the best solution from a range of options (i.e., given a list of desired parameters, preferences, or a combination thereof) that satisfies the necessary business requirements. However, this implicitly assumes that the best option(s) can be predicted with the benchmarked data which is a potentially problematic assumption. Furthermore, it assumes the best option(s) are static (i.e., that they are defined by a set of parameters such as cost, accuracy, and/or time to completion) which can be measured at one time and will remain unchanged. In reality, there may be much variation since the available options are highly dynamic with entry of new crowdsourcing platforms, platform improvements and/or pricing changes in the crowdsourcing platforms. This variation occurs not only across platforms, but also within a single platform depending on parameters such as time of day, day of the week, and even current events. In order to provide the best service it is imperative to have flexibility and adapt to the changing business requirements so that the best service is offered at all times, something that the state-of-the-art does not offer.